Me!

Hi, I’m Dr. Rehnberg!

• I am a transplant to the west coast – PA to MO to MI to CA.

• My favorite things are being outside, drinking tea, and watching reality tv.

• I am teaching this course for the first time – please bear with me as I get materials ready for Canvas.

On a personal note…

I have a genetic, degenerative eye disease called Stargardt disease, which causes me to have poor vision, even with corrective lenses.

What this means for you:

• When I am helping you on your computer, please make the font large and turn the brightness up.

• I have difficulty recognizing faces – please be patient!

Questions?

You!

I am looking forward to reading your introductions on Canvas Discussions!

• Please read the intros of your classmates so you can discover who you will be learning with this quarter.

What is R?

• R is a programming language designed originally for statistical analyses.
• R was created by Ross Ihaka and Robert Gentleman in 1993.
  • Their names are why it’s called R.
• R was formally released by the R Core Group in 1997.
  • This group of 20-ish volunteers are the only people who can change the base (built-in) functionality of R.

Strengths

R’s strengths are…

… handling data with lots of different types of variables.

… making nice and complex data visualizations.

… having cutting-edge statistical methods available to users.

Weaknesses

R’s weaknesses are…

… performing non-analysis programming tasks, like website creation (python, ruby, …).

… hyper-efficient numerical computation (matlab, C, …).

… being a simple tool for all audiences (SPSS, STATA, JMP, minitab, …).

But wait!

Packages

The heart and soul of R are packages.

• These are “extra” sets of code that add new functionality to R when installed.
• “Official” R packages live on the Comprehensive R Archive Network, or CRAN.
• But anyone can write and share new code in “package form” (more later).

Packages

To install a package use:

install.packages(tibble)

library(tibble)

Open-Source

Importantly, R is open-source.

• There is no company that owns R, like there is for SAS or Matlab.
  • (Python is also open-source!)
• This means nobody can sell their R code!
  • But you can sell “helpers” like RStudio.
  • And you can keep code private within an organization or company.

This means packages are created by users like you and me!

Open-Source

Being a good open-source citizen means…

… sharing your code publicly when possible (later in this course, we’ll learn about GitHub!).

… contributing to public projects and packages, as you are able.

… creating your own packages, if you can.

… using R for ethical and respectful projects.

R Basics: Data Types

• A value is a basic unit of stuff that a program works with.

• Values have types:

  1. logical / boolean: FALSE/TRUE or 0/1 values.

  2. integer: whole numbers.

  3. double / float / numeric: decimal numbers.

     • float: “floating-point value”.

     • double: floating-point value with more precision.

     • numeric: decimal value, regardless of precision.

  4. character / string - holds text, usually enclosed in quotes.

R Basics: Variables

Variables are names that refer to values.

• A variable is like a container that holds something - when you refer to the container, you get whatever is stored inside.

• We assign values to variables using the syntax object_name <- value.

  • You can read this as “object name gets value” in your head.

message <- "So long and thanks for all the fish"
year <- 2025
the_answer <- 42
earth_demolished <- FALSE

R Basics: Data Structures

Homogeneous: every element has the same data type.

• Vector: a one-dimensional column of homogeneous data.

• Matrix: the next step after a vector - it’s a set of homogenous data arranged in a two-dimensional, rectangular format.

R Basics: Indexing

We use square brackets ([]) to access elements within data structures.

• In R, we start indexing from 1.

vec[4]    # 4th element
vec[1:3]  # first 3 elements

mat[2,6]  # element in row 2, col 6
mat[,3]   # all elements in col 3

li[[5]]    # 5th element

df[1,2]     # element in row 1, col 2
df[17,]     # all elements in row 17
df$calName  # all elements in the col named "colName"

R Basics: Logic

We can combine logical statements using and, or, and not.

• (X AND Y) requires that both X and Y are true.

• (X OR Y) requires that one of X or Y is true.

• (NOT X) is true if X is false, and false if X is true.

x <- c(TRUE, FALSE, TRUE, FALSE)
y <- c(TRUE, TRUE, FALSE, FALSE)

x & y   # AND

[1]  TRUE FALSE FALSE FALSE

x | y   # OR

[1]  TRUE  TRUE  TRUE FALSE

!x & y  # NOT X AND Y

[1] FALSE  TRUE FALSE FALSE

What is RStudio?

RStudio is an IDE (Integrated Developer Environment).

• This means it is an application that makes it easier for you to interact with R.

History of RStudio

• RStudio was released in 2011 by J.J. Allaire.

• In 2014, RStudio hired Hadley Wickham as Chief Data Scientist. They now employ around 20 full-time developers.

• Recall: You can not sell R code, so packages created by RStudio’s team are freely available.

• They make money off the IDE and other helper software.

• In 2020, RStudio became a PBC (Public Benefit Corp), meaning they are legally obligated to support education and open-source development.

What is a directory?

• A directory is just a fancy name for a folder.

• Your working directory is the folder that R “thinks” it lives in at the moment.

getwd()

• If you save things you have created, they save to your working directory by default.

Paths

• A path describes where a certain file or directory lives.

[1] "/Users/zrehnber/Library/CloudStorage/OneDrive-CalPoly/STAT 331:531/S23/W1"

This file lives in my user files Users/…

…on my account zrehnber/ …

…in my OneDrive OneDrive - Cal Poly …

…in a series of organized folders.

Manage your Class Directory

Create a directory for this class!

• Is it in a place you can easily find it?

• Does it have an informative name?

• Are the files inside it well-organized?

The Beauty of R Projects

• An R Project is basically a “flag” planted in a certain directory.

• When you double click a .Rproj file, it:

RStudio Projects & Reproducibility

RStudio Projects are great for reproducibility!

• You can send anyone your folder with your .Rproj file and they will be able to run your code on their computer.

• We will be using RStudio Projects throughout this course.

Principles of Reproducibility

You can to send your project to someone else, and they can jump in and start working right away.

• This involves:

  1. Files are organized and well-named.

  2. References to data and code work for everyone.

  3. Package dependency is clear.

  4. Code will run the same every time, even if data values change.

  5. Analysis process is well-explained and easy to read.

Good practice

• Organize your folders carefully, and name them meaningfully:
  • /User/zrehnber/Stat331/lab1/ rather than Desktop/stuff/
• Use R Projects liberally - put one in the “main” folder for each project

Bad practice

If you put something like this at the top of your .qmd file (more on Quarto later), I will set your computer on fire:

setwd("/User/reginageorge/Desktop/R_Class/Lab_1/")

• Setting working directory by hand = BAD!

• That directory is specific to you!

• R Markdown and Quarto (more on these later) ignore this code when knitting!

Did you leave off a parenthesis?

seq(from = 1, to = 10, by = 1

seq(from = 1, to = 10, by = 1

Error: <text>:2:0: unexpected end of input
1: seq(from = 1, to = 10, by = 1
   ^

seq(from = 1, to = 10, by = 1)

 [1]  1  2  3  4  5  6  7  8  9 10

Did you leave off a comma?

seq(from = 1, to = 10 by = 1)

seq(from = 1, to = 10 by = 1)

Error: <text>:1:23: unexpected symbol
1: seq(from = 1, to = 10 by
                          ^

seq(from = 1, to = 10, by = 1)

 [1]  1  2  3  4  5  6  7  8  9 10

Did you make a typo? Are you using the right names?

sequence(from = 1, to = 10, by = 1)

sequence(from = 1, to = 10, by = 1)

Error in sequence.default(from = 1, to = 10, by = 1): argument "nvec" is missing, with no default

seq(from = 1, to = 10, by = 1)

 [1]  1  2  3  4  5  6  7  8  9 10

Are you using the right input that the function expects?

sqrt(‘1’)

sqrt('1')

Error in sqrt("1"): non-numeric argument to mathematical function

sqrt(1)

[1] 1

Are you expecting the right output of the function?

my_obj <- seq(from = 1, to = 10, by = 1)

my_obj(5)

Error in my_obj(5): could not find function "my_obj"

my_obj[5]

[1] 5

Messages

Just because you see scary red text, this does not mean something went wrong! This is just R communicating with you.

library(lme4)

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

Warnings

Often, R will give you a warning.

• This means that your code did run…

• …but you probably want to make sure it succeeded.

my_vec <- c("a", "b", "c")

my_new_vec <- as.integer(my_vec)

Warning: NAs introduced by coercion

my_new_vec

[1] NA NA NA

Errors

If the word Error appears in your message from R, then you have a problem.

• This means your code could not run!

my_vec <- c("a", "b", "c")

my_new_vec <- my_vec + 1

Error in my_vec + 1: non-numeric argument to binary operator

Parlez-vous ERROR?

R says…

Error: Object some_obj not found.

some_ojb <- 1:10

mean(some_obj)

Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'mean': object 'some_obj' not found

R says…

Error: Object of type ‘closure’ is not subsettable.

mean[1, 2]

Error in mean[1, 2]: object of type 'closure' is not subsettable

R says…

Error: Non-numeric argument to binary operator.

"a" + 2

Error in "a" + 2: non-numeric argument to binary operator

What if none of these solved my error?

1. Look at the help file for the function!

2. When all else fails, Google your error message.

• Leave out the specifics.

• Include the function you are using.

Try it…

What’s wrong here?

matrix(c("a", "b", "c", "d"), num_row = 2)

Error in matrix(c("a", "b", "c", "d"), num_row = 2): unused argument (num_row = 2)

PA 1: Find the Mistakes

The components of the Practice Activity are described below:

Part One:

This file has many mistakes in the code. Some are errors that will prevent the file from knitting; some are mistakes that do NOT result in an error.

Fix all the problems in the code chunks.

Part Two:

Follow the instructions in the file to uncover a secret message.

Submit the name of the poem as the answer to the Canvas Quiz question.

To do…

• Review Pre-reading + Chapter 1: Introduction
• Concept Checks 1.1 - 1.4
  • Due Wednesday (1/11) at 8AM
• PA 1: Find the Mistakes
  • Due Wednesday (1/11) at 11:59PM
• Bonus Challenge 0: FizzBuzz (+5)
  • Due Saturday (1/21) at 11:59pm

Wednesday, January 11th

Today we will…

• Clarifying Questions
  • Syllabus
  • Chapter 1 Reading
  • PA 1: Find the Mistakes
• Scripts + Notebooks
• Lab 1: Introduction to Quarto
• Challenge 1: Modifying your Quarto Document

Scripts + Notebooks

Scripts

Scripts are files of code that are meant to be run on their own.

File > New File > R Script

# indicates a comment

Scripts can be run in Rstudio by clicking the Run button at the top of the editor window when the script is open.

You can also run code interactively in a script by: + highlighting lines of code (or even just placing your cursor on a line of code) and hitting run or cntrl + enter

Notebooks

Notebooks are an implementation of literate programming.

Allows you to integrate code, output, text, images, etc. into a single document.

Reproducibility!

What is Markdown?

Markdown (without the “R”) is a markup language. This means special symbols and formatting to pretty documents.

Markdown files have the .md extension.

What is R Markdown?

R Markdown (with the “R”) uses regular markdown, plus it can run and display R code. (Other languages, too!)

R Markdown files have the .Rmd extension.

##

We don’t talk about Quarto

What is Quarto?

Quarto unifies and extends the RMarkdown ecosystem.

Quarto is the next generation RMarkdown.

??? It unifies it for people who love R Markdown by reducing little points of friction, so that websites, books, and slides all have a common approach.

And it extends it for people who don’t know RMarkdown by being a friendly way to work reproducibly and publish documents that have text and code in the same place

Think of all the packages from the R Markdown universe that you’ve come to love and rely on over the years. Each package addresses a different need and offers a different output: for example, you can use blogdown or distill for creating websites and blogs, bookdown for writing a book, rticles for writing journal articles, etc.

Quarto unifies the functionality from these packages,

building on Pandoc on the technical side

and on the human side – over a decade of experience developing, maintaining, and tweaking these packages as well as community feedback R Markdown extension packages.

Such a unification effort presents a fantastic opportunity to identify and address gaps functionality and inefficiencies in user experience, and the process of building Quarto has been no different.

Quarto highlights

• Consistent implementation of attractive and handy features across outputs: tabsets, code-folding, syntax highlighting, etc.

• More accessible defaults as well as better support for accessibility

• Guardrails, particularly helpful for new learning: YAML completion, informative syntax errors, etc.

• Support for other languages like Python, Julia, Observable, and more via Jupyter engine for executable code chunks.

???

Some highlights of these improvements include

–> consistent implementation of attractive and handy features across all outputs, like tabsets, code-folding, and syntax highlighting,

–> more accessible defaults as well as better support for creating accessible documents,

–> guardrails that are particularly helpful for new learners, like YAML completion and informative syntax errors,

–> and perhaps most excitingly for those who are not coming from the R ecosystem, Quarto offers support for other languages like Python, Julia, Observable, and more via the Jupyter engine for executable code chunks. And it’s designed to be expandable to more engines and languages, even those that might not exist today.

[pause] So by now I’m assuming many of you have already started playing with Quarto, or you’re waiting for me to do that. So, let’s dive in and see what all the Quarto fuss is about!

Rendering in Quarto

Quarto CLI orchestrates each step of rendering

???

Now that you’ve all had a chance to see Quarto in action, you might be wondering, “how are R Markdown and Quarto different”?

The main difference is that with R Markdown, the R package rmarkdown does the heavy lifting of going from the source Rmd file to the the output, using knitr for evaluating the code chunks.

–> With Quarto, on the other hand, the Quarto command line interface, or the Quarto CLI, does the orchestration of processing executable code chunks with either knitr or jupyter and then converting the resulting markdown file to the desired output.

While this is technically impressive, I’ll be honest, it’s not exactly what sparked my interest in Quarto in the first place.

Quarto formats

Quarto makes moving between formats straightforward

.pull-left[ Document

title: "Lesson 1"
format: html

Presentation

title: "Lesson 1"
format: revealjs

].pull-right[ Website

project:
  type: website

website: 
  navbar: 
    left:
      - lesson-1.qmd

]

???

What did spark my interest was how how straightforward it is with Quarto to move between output formats.

As an educator, two things are of utmost importance to me about the tools I use to create my materials: reproducibility and ease of transition between output formats, like documents to slides to websites to books.

Over the last year of using Quarto for pretty much everything, I’ve felt like I’ve finally found the tool that lets me go from one output type to the other with minimal, if any, futzing around with my source code beyond the yaml. For example, here are common things I produce

–> a lesson in document form
–> the same content in presentation form
–> the same content on a page in a website,

and you can see that all that needed to change going between these formats is a few lines in the yaml. Nothing in the content part of my document. No slide breaks to remove, no citation style to change, no headings to re-level. This ease of transition has freed up time to focus my time on content, and that, folks, is the dream!

Quarto preview

Welcome to Quarto!

Quarto components

Markdown in Quarto

Formatting the text in your document

Markdown is a markup language. This means special symbols and formatting to pretty documents.

*text* – makes italics

**text** – makes bold text

# – makes headers

 – includes images or HTML links

< > – embeds URLs

R code options in Quarto

Hashpipe

#|

Rendering your Quarto Document

???

When you click Render, here is what happens:

• Your file is saved.
• The R code written in your .qmd file gets run.
  • Any code you ran already doesn’t “count”; we start from scratch.
  • The code is run in order.
• A new file is created.
  • If your Quarto file is called “Lab1.qmd”, then a file called “Lab1.html” will be created.
  • This will save in the same folder as “Lab1.qmd”.

Lab 1: Introduction to Quarto + Challenge 1: Modifying your Quarto Document

To do…

• PA 1: Find the Mistakes
  • Due TONIGHT, Wednesday (1/11) at 11:59PM
• Lab 1: Introduction to Quarto
  • Due Friday (1/13) at 11:59pm
• Challenge 1: Modifying Your Quarto Document (+10)
  • Due Saturday (1/14) at 11:59pm
• Read Chapter 2: Importing Data + Basics of Graphics
  • Concept Check 2.1 + 2.2 due Tuesday (1/17) at 8AM
• Bonus Challenge 0: FizzBuzz (+5)
  • Due Saturday (1/21) at 11:59pm